Scaling Point-in-Time Language Models: Enabling Economic Evaluation of Embeddings

arXiv:2607.11889 · cs.CL, cs.AI · Submitted 2026-04-24 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-04-24

Updated: 2026-10-12

Code: https://github.com/KellerJordan/modded-nanogpt

Terminology

• Benchmark — 1× in this paper · explained in 2 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

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• Scaling Instruction-Finetuned Language Models
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• GPTScore: Evaluate as You Desire
• Representation Degeneration Problem in Training Natural Language Generation Models
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling
• Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis
• Continual Pre-Training of Large Language Models: How to (re)warm your model?
• Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
• Chronologically Consistent Large Language Models
• Instruction Tuning Chronologically Consistent Language Models
• Scaling Laws for Autoregressive Generative Modeling
• Training Compute-Optimal Large Language Models
• Scaling Laws for Neural Language Models

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